Prototyping of wavelet transform, artificial neural network and fuzzy logic for power quality disturbance classifier

M. B. I. Reaz*, F. Choong, M. S. Sulaiman, F. Mohd-Yasin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


Identification and classification of voltage and current disturbances in power systems are important tasks in their monitoring and protection. Introduction of knowledge-based approaches, in conjunction with signal processing and decision fusion techniques, enable us to identify delicate power quality related events. This article focuses on the application of wavelet transform technique to extract features from power quality disturbance waveforms and their classification using a combination of artificial neural network and fuzzy logic. The disturbances of interest include sag, swell, transient, fluctuation and interruption waveform. The system is modelled using VHDL and synthesized to Mercury EP1M120F484C5 FPGA, tested and validated. Comparisons, verification and analysis on disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.19%. This novel and efficient method, and also implementation of the method in hardware based on FPGA technology, showed improved performance over existing approaches for power quality disturbance detection and classification.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalElectric Power Components and Systems
Issue number1
Publication statusPublished - 2007


  • Artificial neural network
  • Classification
  • Feature extraction
  • FPGA
  • Fuzzy logic
  • Power quality
  • VHDL
  • Wavelet transform

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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